Tuesday, June 14, 2016

Highlights
• Non-linear effects of open view, ocean view, and green view, are estimated.
• The spatial multilevel additive model is applied for a hedonic analysis.
• Not only poor green view, but also excessive green view has a negative impact.
• How to evaluate 3D view to trees with remotely sensed data is demonstrated.

Abstract
This paper attempts to assess the value of urban views in a bay city (Yokohama), Japan. Firstly, three types of views, open view (goodness of visibility), green view (visibility of open space), and ocean view (visibility of ocean), were quantified employing the viewshed analysis implemented on the GIS with airborne LiDAR data and 0.5 m × 0.5 m high resolution aerial photos. Secondly, hedonic analyses were conducted to test the capitalization of value of those views into condominium prices using the spatial multilevel additive regression (SMAR) model, where possible non-linearity, multilevel structure of condominiums (unit-building), and spatial dependence were considered. This study implies that “very nice” open view (in terms of the amount of visibility) and ocean view may have a positive premium, whereas “slightly nice” open and ocean views may not. Also, a “moderate amount” of green view may raise condominium prices, but “poor” and “too much” green view may reduce condominium prices. These results indicate that the effects of views are indeed non-linear, and therefore it may be misleading to interpret the results obtained by linear models as existing studies have done....

https://en.wikipedia.org/wiki/File:Minato_Mirai_In_Blue.jpg
Our viewshed analysis was conducted by employing the DSM and DTM shown in Fig. 2 and Fig. 3,
respectively. The DSM describes the height of the surface defined as
the sum of the height of the ground and the height of the objects on it,
and the DTM describes the height of the ground surface only. They are
created from airborne LiDAR data through GIS data processing. Their
spatial resolution (mesh block size) is approximately 0.5 m × 0.5 m.
Using these data, we applied the viewshed analysis to evaluate open
view, green view, and ocean view. The latter two were evaluated by
counting the number of mesh blocks of green and ocean which were visible
from each unit. Open view was evaluated by counting the number of mesh
blocks of visible DSM from each unit.

...

Table 4.
Parameter estimation results.

MR

SMR

SMAR

Coef.

t-value

Coef.

t-value

Coef.

t-value

Intercept

3.80200

21.51

***

2.79200

12.61

***

3.19000

15.85

***

Area

1.12100

421.19

***

1.12000

421.26

***

1.11800

422.86

***

Open view

0.02581

7.03

***

0.02615

7.13

***

Green view

0.00320

0.90

0.00297

0.84

Ocean view

0.00223

4.89

***

0.00226

4.96

***

Floor

0.00971

44.95

***

0.00970

44.87

***

Num. dev.

0.01255

1.05

0.00582

0.51

0.00667

0.60

Major dev.

0.04764

3.99

***

0.04291

3.77

***

0.04982

4.44

***

SRC

−0.02254

−1.52

−0.02365

−1.68

.

−0.02262

−1.63

WRC

0.03108

0.76

0.03547

0.92

0.03558

0.94

Station

0.00809

1.25

−0.00378

−0.58

−0.00073

−0.12

Central distance

0.00845

0.85

−0.06025

−2.19

*

−0.06400

−2.96

**

Green

−0.01583

−1.18

0.06430

3.67

***

0.06338

3.95

***

Park

−0.00846

−1.83

.

−0.00625

−1.35

−0.00897

−2.00

*

Ocean

−0.02005

−2.97

**

0.01320

0.95

0.01277

1.05

C1 res.

−0.03670

−1.93

.

−0.01818

−1.00

−0.02054

−1.16

C1 high

0.04437

2.07

*

0.04097

1.97

*

0.02994

1.48

Semi Ind.

−0.05483

−2.58

**

−0.05243

−2.58

**

−0.04733

−2.37

*

The estimates of the view variables (“Open view”; “Green view”; and
“Ocean view”) in the MR and SMR models are rather similar. The results
indicate that “Open view” and “Ocean view” are positively significant at
the 0.1% level whereas “Green view” is also positive but does not have a
statistically significant influence. While these estimates are
interpretable, the non-linear effects in the SMAR model, plotted in Fig. 11 (x-axis: values of regressors: zq,i-j; y-axis: the estimated non-linear effects of each regressor: the estimates of f(zq,i-j)), are highly nonlinear. In other words, there is a possible danger in interpretation of these linear estimates

...

Fig. 11 indicates that the effect of “Open view” is non-linear, that is, f(Open
view) increases non-linearly with the increase of the value of “Open
view”. We can see that if the value of “Open view” is less than around
12, f(Open view) is constantly negative, but after around 12, the effect of f(Open view) becomes positive and it increases rapidly with the increase of the value of “Open view”. It means that very nice view (in terms of the amount of visibility) may be capitalized into condominium prices, but slightly nice
view may not have any positive impacts. Such information could be
useful for condominium developers and/or urban designers. With regard to
“Ocean view”, its effect is fairly linear except for the low-value
regions where ocean is not visible. Hence the results suggest that
“Ocean view”, if the ocean is visible, may linearly increase the value
of condominiums.

With regard to the
coefficient estimates of variables other than view, the estimates for
“Area”, “Floor” and “Major dev.” are positive and statistically
significant at the 0.1% level and “Semi-Ind”, “HH pop.”, “Ind. 2 ratio”
are negative and statistically significant at the 0.1% level, whereas
the levels of significance of the other variables are fairly different
among the models. The
positive sign of “Floor” demonstrates that some factors other than
visibility inflate the prices of higher floors. These factors would
include fresh air (Kei, Wing, Yung, & Chung, 2006),
better ventilation, much sunlight, better security, and lower humidity,
which is important in Japan. Furthermore, high-rise units are often
penthouse units, which have a positive premium (Conroy, Narwold, & Sandy, 2013). Thus, the positive sign of “Floor” is reasonable.

...

Some earlier studies measured visibility using a dummy variable, which
takes one if a focused object is visible and zero if it is not visible
(e.g., Benson, Hansen, Schwartz, & Smersh, 1998; McLeod, 1984). Other studies evaluated visibility based on field investigations. For instance, Tyrvainen and Miettinen (2000) used a field investigation to obtain the visibility information from the window of a unit of a condominium, whereas Luttik (2000) extracted such information from maps, complemented by field investigations....

...

Recent studies have employed more sophisticated visibility evaluation approaches (Yamagata et al., 2015): the isovist analysis, which has been developed mainly in architectural and urban studies (Benedikt, 1979), and the viewshed analysis, which has been developed mainly in landscape studies (Lynch, 1976).
Isovist is defined as “the set of all points visible from a given
vantage point in space and with respect to an environment” (Benedikt, 1979).
A conventional isovist analysis evaluates views in a two dimensional
(2D) space, and therefore one of the limitations of the conventional
isovist analysis is ignorance of the third dimension (i.e.,
height)—capturing only a 2D horizontal slice of human perception (Yu, Han, & Chai, 2007; Yang, Putra, & Li, 2007). Note that, recently, some extensions that cope with this problem have been proposed (e.g., Bhatia, Chalup, & Ostwald, 2013; Morello & Ratti, 2009).
On the other hand, the viewshed analysis tries to quantify a three
dimensional (3D) view by examining whether each cell in a 3D raster is
visible or not from an observation point.

Aguanomics

Robert Stavins Blog

Gadget

BlogUpp!

Subscribe Via Blogger

Networked Blogs

Gadget

This content is not yet available over encrypted connections.

ABOUT US

Environmental Valuation & Cost Benefit News covers legal, academic, and regulatory developments pertaining to the valuation of environmental amenities and disamenities, such as clean air, trees, parks, congestion, and noise. We apprise the reader about ways in which costs and benefits are measured, and the results of empirical studies. We hope that this information will allow public and private organizations to comprehend the risks and benefits of various actions, help disputants to resolve conflicts equitably and efficiently, and improve the quality of public policies.

We will only discuss issues related to the empirical quantification of private and social costs and benefits and damages, and summarize information from daily newspapers, academic journals, legal publications, court decisions, professional newsletters commissioned studies, and on-line services. This newsletter is dedicated to the principle that all policies place values upon life, liberty, and the pursuit of happiness. We believe that more information, explicit specification of assumptions, and rigorous analysis can help our society to better meet these ends. This site will increasingly serve, in conjunction with others, as a valuation database. We will include a wide range of studies, including non-environmental reports, because omission of a factor effectively values it at zero, and biases decisions..